class MapDataGeneration(object):
def __init__(self):
self.color = "235"
self.lightness = "50%"
self.saturation = "100%"
self.count_generation = CountGeneration()
def generate_map_data_array(self,collected_features_array,feature_name):
feature_counts = self.count_generation.generate_counts(collected_features_array, feature_name)
max_count = self.get_max_count(feature_counts)
map_data_array = []
# yellow = Color("lime")
# red = Color("red")
# color_range = list(yellow.range_to(red, max_count))
for label in feature_counts:
feature_dir = {}
#saturation = int(math.log(feature_counts[label]) / math.log(max_count) *100)
#color = color_range[feature_counts[label]-1]
ratio = float(feature_counts[label])/max_count
color = self.get_color(ratio)
hue = color.hsl[0] * 360
feature_dir["color"] = "hsl(" + str(hue)+", "+ self.saturation+", "+self.lightness+")"
feature_dir["count"] = str(feature_counts[label])
feature_dir["label"] = str(pycountry.countries.get(alpha2=label).alpha3)
map_data_array.append(feature_dir)
return map_data_array
def get_max_count(self,feature_counts):
max_count = 0
for label in feature_counts:
if feature_counts[label] > max_count:
max_count = feature_counts[label]
return max_count
# calculation based on: http://www.andrewnoske.com/wiki/Code_-_heatmaps_and_color_gradients
def get_color(self, value):
# blue rgb
aR = 0
aG = 0
aB = 255
# red rgb
bR = 255
bG = 0
bB = 0
red = (float(bR - aR) * value + aR)/255
green = (float(bG - aG) * value + aG)/255
blue = (float(bB - aB) * value + aB)/255
return Color(rgb=(red,green,blue))
#import json
#
#with open("/srv/http/articles/de/Krimkrise/analysis.json") as json_file:
# json_data = json.load(json_file)
# print(json_data)
#
#map_data_generation = MapDataGeneration()
#
#map_data_array = map_data_generation.generate_map_data_array(json_data, "classification-general")
#
#json_writer.write_json_file(map_data_array, "/srv/http/articles/de/Krimkrise/map-data.json")
class MapDataGeneration(object):
def __init__(self):
self.color = "235"
self.lightness = "50%"
self.saturation = "100%"
self.count_generation = CountGeneration()
def generate_map_data_array(self, collected_features_array, feature_name):
feature_counts = self.count_generation.generate_counts(
collected_features_array, feature_name)
max_count = self.get_max_count(feature_counts)
map_data_array = []
# yellow = Color("lime")
# red = Color("red")
# color_range = list(yellow.range_to(red, max_count))
for label in feature_counts:
feature_dir = {}
#saturation = int(math.log(feature_counts[label]) / math.log(max_count) *100)
#color = color_range[feature_counts[label]-1]
ratio = float(feature_counts[label]) / max_count
color = self.get_color(ratio)
hue = color.hsl[0] * 360
feature_dir["color"] = "hsl(" + str(
hue) + ", " + self.saturation + ", " + self.lightness + ")"
feature_dir["count"] = str(feature_counts[label])
feature_dir["label"] = str(
pycountry.countries.get(alpha2=label).alpha3)
map_data_array.append(feature_dir)
return map_data_array
def get_max_count(self, feature_counts):
max_count = 0
for label in feature_counts:
if feature_counts[label] > max_count:
max_count = feature_counts[label]
return max_count
# calculation based on: http://www.andrewnoske.com/wiki/Code_-_heatmaps_and_color_gradients
def get_color(self, value):
# blue rgb
aR = 0
aG = 0
aB = 255
# red rgb
bR = 255
bG = 0
bB = 0
red = (float(bR - aR) * value + aR) / 255
green = (float(bG - aG) * value + aG) / 255
blue = (float(bB - aB) * value + aB) / 255
return Color(rgb=(red, green, blue))
#import json
#
#with open("/srv/http/articles/de/Krimkrise/analysis.json") as json_file:
# json_data = json.load(json_file)
# print(json_data)
#
#map_data_generation = MapDataGeneration()
#
#map_data_array = map_data_generation.generate_map_data_array(json_data, "classification-general")
#
#json_writer.write_json_file(map_data_array, "/srv/http/articles/de/Krimkrise/map-data.json")
collected_features_with_fixed_outliers = article_extraction.fix_outliers(collected_features_with_fixed_outliers,"classification-general","classification-general-fixed",features)
collected_features_array = article_extraction.get_as_array(collected_features_with_fixed_outliers)
if len(collected_features_array) > 0:
# generate directories if they don't exist
if not os.path.exists(article_path):
os.makedirs(article_path)
if not os.path.exists(language_path):
os.makedirs(language_path)
json_writer.write_json_file(collected_features_array, article_analysis_path)
count_features = ["ip-location","tld-location","website-language","classification-fixed","classification-general-fixed"]
for count_feature in count_features:
classification_general_counts = count_generation.generate_counts(collected_features_array, count_feature)
classification_general_counts_array = count_generation.get_as_array(classification_general_counts, 20)
article_count_path = os.path.join(article_path,"counts-"+count_feature+"-top-20.json")
json_writer.write_json_file(classification_general_counts_array, article_count_path)
# generate map data
map_data = map_data_generation.generate_map_data_array(collected_features_array,"classification-general-fixed")
article_map_data_path = os.path.join(article_path,"map-data.json")
json_writer.write_json_file(map_data, article_map_data_path)
# get execution date
now = datetime.datetime.now()
time_info = {}
time_info["analysis-date"]= now.strftime("%Y-%m-%d")
time_info["analysis-time"]= now.strftime("%H:%M:%S")
